Institutions of higher education across the country have partnered with predictive analytics companies to improve data-informed decision making on campus. These institutions are making sweeping changes to decision making processes based on predictive algorithms with the goal of improving student persistence and graduation rates. The purpose of this qualitative inquiry was to explore the experience of leaders using predictive analytics software working at a midsized open access state college in the Southeast. Following a purposeful sampling process, interviews were conducted to gather data. Bolman and Deal's (2017) four-frame model was employed to understand how higher education leaders construct meaning around their experiences with data. Through a phenomenological analysis of leadership experiences, Bolman and Deal's (2017) four-frame leadership model helped uncover the ways in which leadership approaches emerged as part of a data-informed decision-making process. The questions addressed by this study relate to the effect of using predictive analytics as part of participants' administrative work analyzing data. The research participants and location were selected due to access and familiarity. Additionally, only a limited amount of research exploring the experiences of higher education leadership using predictive analytics exists. The findings of this study add to the emerging literature on the use of predictive analytics in higher education.
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Doctor of Education (Ed.D.)
College of Community Innovation and Education
Educational Leadership and Higher Education
Educational Leadership; Higher Education Track
Length of Campus-only Access
Doctoral Dissertation (Campus-only Access)
Barnes, Elizabeth, "An Exploration of Higher Education Leaders Using Predictive Analytics Software at an Open Access State College" (2021). Electronic Theses and Dissertations, 2020-. 1318.
Restricted to the UCF community until June 2025; it will then be open access.